Aspects of the exemplary embodiment disclosed herein relate to a method and system for classifying a digital image of a three-dimensional object such as a vehicle according to object type, such as vehicle tree.
Vehicle recognition is a challenging task with many useful applications. With the explosive growth of new vehicle models every year, recognizing different types of vehicle models is a difficult problem and people have been working on developing machine algorithms to recognize vehicles for years.
Visual attributes have been shown to be effective for many computer vision problems. Attributes are considered discriminative and semantic mid-level image representations.
Previously, local discriminative regions for image classification tasks have been explored. In Bangpeng Yao et al., “Combining Randomization and Discrimination for Fine-Grained Image Categorization”, CVPR, 2011, pp. 1577-1584, the authors propose to use a random forest with dense sampling to discover discriminative regions. The random forest combines thousands of region classifiers together, and thus improves the classification performance using only low-level image features.
Recently there has been work on automatic “part discovery” for different object categories. Subhransu Maji et al., “Part Discovery from Partial Correspondence”, CVPR, 2013, describes an approach to collect pairs of user click annotations on landmark images. The method disclosed in Subhransu Maji et al., uses a SVM (Support Vector Machine) method to find salient regions, while using the click pair information to jointly infer object parts. One problem is that the Subhransu Maji et al., method does not optimize classification accuracy at the object level.
Provided herein are methods and systems to automatically discover a mid-level image representation, i.e., a set of attributes, using constrained multiple instance Support Vector Machines (SVMs).
Incorporation By Reference
The following references, the disclosures of which are incorporated in their entireties by reference, are mentioned:
U.S. Patent Application Publication No. 2012/0308124, published Dec. 6, 2012, by Belhumeur et al., entitled “Method and System for Localizing Parts of an Object in an Image for Computer Vision Applications”;
Kun Duan et al., “Discovering Localized Attributes for Fine-grained Recognition”, CVPR, pages 3474-3481, 2012;
U.S. Patent Application Publication No. 2013/0016877, published Jan. 17, 2013, by Feris et al., entitled “Multi-View Object Detection Using Appearance Model Transfer from Similar Scenes”;
Chunhui Gu et al., “Discriminative Mixture-of-Templates for Viewpoint Classification”, ECCV (5), pages 408-421, 2010;
Subhransu Maji et al., “Part Discovery from Partial Correspondence”, CVPR, 2013;
U.S. Pat. No. 8,358,808, issued Jan. 22, 2013, by Malinovskiy et al., entitled “Video-Based Vehicle Detection and Tracking Using Spatio-Temporal Maps”;
WO 2010/034065 A1, by Kostia et al., published Apr. 1, 2010, entitled “Detection of Vehicles in an Image”;    Gaurav Sharma et al., “Expanded Parts Model for Human Attribute and Action Recognition in Still Images”, 2013;
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Bangpeng Yao et al., “Combining Randomization and Discrimination for Fine-Grained Image Categorization”, 2011, pages 1577-1584; and
U.S. Pat. No. 7,764,808, issued Jul. 27, 2010, by Zhu et al., entitled “System and Method for Vehicle Detection and Tracking”.